In this paper, we propose to implement a meteorological monitoring station using embedded systems. This model is possible thanks to different sensors that enable us to measure several environmental parameters, such as i) relative humidity, ii) average ambient temperature, iii) soil humidity, iv) rain occurrence, and v) light intensity. The proposed system is based on a field-programmable gate array device (FPGA). The proposed design aims at ensuring highresolution data acquisition and at predicting samples with precision and accuracy in real-time. To present the collected data, we develop also a web application with a simple and friendly user interface. ⭐ For more information visit our blog: https://vasanza.blogspot.com/

1. FPGA Based Meteorological Monitoring
Station
Víctor Asanza , Rebeca Estrada Pico, Danny Torres, Steven Santillan and Juan Cadena
Escuela Superior Politécnica del Litoral, ESPOL, Guayaquil, Ecuador
Centro de Tecnologías de Información, CTI
Facultad de Ingeniería en Electricidad y Computación, FIEC

2. Topics
• Introduction
• Related Work
• High-resolution Data Acquisition
• Methodology and System Model
• Numerical Results
• Conclusions
FPGA Based Meteorological Monitoring
Station

3. Introduction
• Field Programmable Gate Array (FPGA)
• System on Chip (SoC)
• Nios II Processor
• Hard Processor System (HPS)
• Advanced RISC Machine (ARM)

4. Related Work
• M. Haefke et al. proposed a weather station
based on ZigBee, to monitor variables such as
temperature, pressure, sunlight intensity, and
humidity [6].
• Aziz et Al. proposed similar system based on
FPGA considering humidity and light intensity to
determine when to water the plants, using
neural networks [8].
• Shaari et Al. proposed a Artificial Neural
Network (ANN) based on FPGA for the
prediction of Solar Radiation using Data from
sunlight duration and average temperature [10].

5. High-resolution Data Acquisition
Dataset Citation:
Juan Cadena, Steven Santillan, Víctor Asanza, Rebeca
Estrada, July 11, 2021, "Weather Monitoring Station For
Farms And Agriculture", IEEE Dataport, doi:
https://dx.doi.org/10.21227/mdfs-ya42.
* Values are sampled each 30 seconds and saved as CSV.

6. Methodology and System Model

7. Methodology and System Model

8. Numerical Results

9. Numerical Results

10. Conclusions

11. For more information
Víctor Asanza
Mail: vasanza@espol.edu.ec
Facultad de Ingeniería en Electricidad y Computación, FIEC
Escuela Superior Politécnica del Litoral, ESPOL
Campus Gustavo Galindo Km 30.5 Vía Perimetral, P.O. Box 09-01-5863
090150 Guayaquil, Ecuador

12. Published in:
https://ieeexplore.ieee.org/abstract/document/9530151

13. Thank you!